{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6c4b7b93",
   "metadata": {},
   "source": [
    "## 介绍\n",
    "麦当劳（McDonald's）是源自美国南加州的跨国连锁快餐店，也是全球最大的快餐连锁店，主要贩售汉堡包及薯条、炸鸡、汽水、冰品、沙拉、水果、咖啡等快餐食品。近年来，越来越多的人意识到快餐食品的不健康性，麦当劳也成了“垃圾食品”的代名词。美国纪录片《Super Size Me》记录了一个人一个月内只吃麦当劳后的身体变化，更引起了人们对于快餐食品营养超标的担忧。本分析旨在通过实证方法评估麦当劳数据集中260个产品的营养成分，我们先从一些标准的数据探索分析开始，之后讨论并使用Plotly绘制交互式散点图以展示不同的营养指标。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "476eeee9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.9.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import plotly.offline as py\n",
    "py.init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "import plotly.tools as tls\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8568ba8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号 #有中文出现的情况，需要u'内容'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "455a3e80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Item</th>\n",
       "      <th>Serving Size</th>\n",
       "      <th>Calories</th>\n",
       "      <th>Calories from Fat</th>\n",
       "      <th>Total Fat</th>\n",
       "      <th>Total Fat (% Daily Value)</th>\n",
       "      <th>Saturated Fat</th>\n",
       "      <th>Saturated Fat (% Daily Value)</th>\n",
       "      <th>Trans Fat</th>\n",
       "      <th>...</th>\n",
       "      <th>Carbohydrates</th>\n",
       "      <th>Carbohydrates (% Daily Value)</th>\n",
       "      <th>Dietary Fiber</th>\n",
       "      <th>Dietary Fiber (% Daily Value)</th>\n",
       "      <th>Sugars</th>\n",
       "      <th>Protein</th>\n",
       "      <th>Vitamin A (% Daily Value)</th>\n",
       "      <th>Vitamin C (% Daily Value)</th>\n",
       "      <th>Calcium (% Daily Value)</th>\n",
       "      <th>Iron (% Daily Value)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Breakfast</td>\n",
       "      <td>Egg McMuffin</td>\n",
       "      <td>4.8 oz (136 g)</td>\n",
       "      <td>300</td>\n",
       "      <td>120</td>\n",
       "      <td>13.0</td>\n",
       "      <td>20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Breakfast</td>\n",
       "      <td>Egg White Delight</td>\n",
       "      <td>4.8 oz (135 g)</td>\n",
       "      <td>250</td>\n",
       "      <td>70</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Breakfast</td>\n",
       "      <td>Sausage McMuffin</td>\n",
       "      <td>3.9 oz (111 g)</td>\n",
       "      <td>370</td>\n",
       "      <td>200</td>\n",
       "      <td>23.0</td>\n",
       "      <td>35</td>\n",
       "      <td>8.0</td>\n",
       "      <td>42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>29</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Breakfast</td>\n",
       "      <td>Sausage McMuffin with Egg</td>\n",
       "      <td>5.7 oz (161 g)</td>\n",
       "      <td>450</td>\n",
       "      <td>250</td>\n",
       "      <td>28.0</td>\n",
       "      <td>43</td>\n",
       "      <td>10.0</td>\n",
       "      <td>52</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Breakfast</td>\n",
       "      <td>Sausage McMuffin with Egg Whites</td>\n",
       "      <td>5.7 oz (161 g)</td>\n",
       "      <td>400</td>\n",
       "      <td>210</td>\n",
       "      <td>23.0</td>\n",
       "      <td>35</td>\n",
       "      <td>8.0</td>\n",
       "      <td>42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Category                              Item    Serving Size  Calories  \\\n",
       "0  Breakfast                      Egg McMuffin  4.8 oz (136 g)       300   \n",
       "1  Breakfast                 Egg White Delight  4.8 oz (135 g)       250   \n",
       "2  Breakfast                  Sausage McMuffin  3.9 oz (111 g)       370   \n",
       "3  Breakfast         Sausage McMuffin with Egg  5.7 oz (161 g)       450   \n",
       "4  Breakfast  Sausage McMuffin with Egg Whites  5.7 oz (161 g)       400   \n",
       "\n",
       "   Calories from Fat  Total Fat  Total Fat (% Daily Value)  Saturated Fat  \\\n",
       "0                120       13.0                         20            5.0   \n",
       "1                 70        8.0                         12            3.0   \n",
       "2                200       23.0                         35            8.0   \n",
       "3                250       28.0                         43           10.0   \n",
       "4                210       23.0                         35            8.0   \n",
       "\n",
       "   Saturated Fat (% Daily Value)  Trans Fat  ...  Carbohydrates  \\\n",
       "0                             25        0.0  ...             31   \n",
       "1                             15        0.0  ...             30   \n",
       "2                             42        0.0  ...             29   \n",
       "3                             52        0.0  ...             30   \n",
       "4                             42        0.0  ...             30   \n",
       "\n",
       "   Carbohydrates (% Daily Value)  Dietary Fiber  \\\n",
       "0                             10              4   \n",
       "1                             10              4   \n",
       "2                             10              4   \n",
       "3                             10              4   \n",
       "4                             10              4   \n",
       "\n",
       "   Dietary Fiber (% Daily Value)  Sugars  Protein  Vitamin A (% Daily Value)  \\\n",
       "0                             17       3       17                         10   \n",
       "1                             17       3       18                          6   \n",
       "2                             17       2       14                          8   \n",
       "3                             17       2       21                         15   \n",
       "4                             17       2       21                          6   \n",
       "\n",
       "   Vitamin C (% Daily Value)  Calcium (% Daily Value)  Iron (% Daily Value)  \n",
       "0                          0                       25                    15  \n",
       "1                          0                       25                     8  \n",
       "2                          0                       25                    10  \n",
       "3                          0                       30                    15  \n",
       "4                          0                       25                    10  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "menu=pd.read_csv('mcdonald_s_menu.csv')\n",
    "menu.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "07b71b62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "该数据集共有260行24列\n"
     ]
    }
   ],
   "source": [
    "print('该数据集共有{}行{}列'.format(menu.shape[0],menu.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7d5c196",
   "metadata": {},
   "source": [
    "每一行代表一样麦当劳产品；24列，包括了产品的类别，名称，大小，以及营养成分（如卡路里，脂肪，胆固醇，钠，碳水化合物，膳食纤维，糖，蛋白质，维他命A，维他命C，钙，铁）等内容。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59db228e",
   "metadata": {},
   "source": [
    "## 数据质量检查\n",
    "首先需要检查数据的质量，例如特征中是否存在空值或缺失值，数值的大小是否合理等等。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e82bcefa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category                         False\n",
       "Item                             False\n",
       "Serving Size                     False\n",
       "Calories                         False\n",
       "Calories from Fat                False\n",
       "Total Fat                        False\n",
       "Total Fat (% Daily Value)        False\n",
       "Saturated Fat                    False\n",
       "Saturated Fat (% Daily Value)    False\n",
       "Trans Fat                        False\n",
       "Cholesterol                      False\n",
       "Cholesterol (% Daily Value)      False\n",
       "Sodium                           False\n",
       "Sodium (% Daily Value)           False\n",
       "Carbohydrates                    False\n",
       "Carbohydrates (% Daily Value)    False\n",
       "Dietary Fiber                    False\n",
       "Dietary Fiber (% Daily Value)    False\n",
       "Sugars                           False\n",
       "Protein                          False\n",
       "Vitamin A (% Daily Value)        False\n",
       "Vitamin C (% Daily Value)        False\n",
       "Calcium (% Daily Value)          False\n",
       "Iron (% Daily Value)             False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "menu.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "466b6b4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Calories</th>\n",
       "      <th>Calories from Fat</th>\n",
       "      <th>Total Fat</th>\n",
       "      <th>Total Fat (% Daily Value)</th>\n",
       "      <th>Saturated Fat</th>\n",
       "      <th>Saturated Fat (% Daily Value)</th>\n",
       "      <th>Trans Fat</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>Cholesterol (% Daily Value)</th>\n",
       "      <th>Sodium</th>\n",
       "      <th>...</th>\n",
       "      <th>Carbohydrates</th>\n",
       "      <th>Carbohydrates (% Daily Value)</th>\n",
       "      <th>Dietary Fiber</th>\n",
       "      <th>Dietary Fiber (% Daily Value)</th>\n",
       "      <th>Sugars</th>\n",
       "      <th>Protein</th>\n",
       "      <th>Vitamin A (% Daily Value)</th>\n",
       "      <th>Vitamin C (% Daily Value)</th>\n",
       "      <th>Calcium (% Daily Value)</th>\n",
       "      <th>Iron (% Daily Value)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>260.000000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>260.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>368.269231</td>\n",
       "      <td>127.096154</td>\n",
       "      <td>14.165385</td>\n",
       "      <td>21.815385</td>\n",
       "      <td>6.007692</td>\n",
       "      <td>29.965385</td>\n",
       "      <td>0.203846</td>\n",
       "      <td>54.942308</td>\n",
       "      <td>18.392308</td>\n",
       "      <td>495.750000</td>\n",
       "      <td>...</td>\n",
       "      <td>47.346154</td>\n",
       "      <td>15.780769</td>\n",
       "      <td>1.630769</td>\n",
       "      <td>6.530769</td>\n",
       "      <td>29.423077</td>\n",
       "      <td>13.338462</td>\n",
       "      <td>13.426923</td>\n",
       "      <td>8.534615</td>\n",
       "      <td>20.973077</td>\n",
       "      <td>7.734615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>240.269886</td>\n",
       "      <td>127.875914</td>\n",
       "      <td>14.205998</td>\n",
       "      <td>21.885199</td>\n",
       "      <td>5.321873</td>\n",
       "      <td>26.639209</td>\n",
       "      <td>0.429133</td>\n",
       "      <td>87.269257</td>\n",
       "      <td>29.091653</td>\n",
       "      <td>577.026323</td>\n",
       "      <td>...</td>\n",
       "      <td>28.252232</td>\n",
       "      <td>9.419544</td>\n",
       "      <td>1.567717</td>\n",
       "      <td>6.307057</td>\n",
       "      <td>28.679797</td>\n",
       "      <td>11.426146</td>\n",
       "      <td>24.366381</td>\n",
       "      <td>26.345542</td>\n",
       "      <td>17.019953</td>\n",
       "      <td>8.723263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>210.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>2.375000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>107.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>340.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>22.250000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>21.250000</td>\n",
       "      <td>865.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1880.000000</td>\n",
       "      <td>1060.000000</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>182.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>575.000000</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>3600.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Calories  Calories from Fat   Total Fat  Total Fat (% Daily Value)  \\\n",
       "count   260.000000         260.000000  260.000000                 260.000000   \n",
       "mean    368.269231         127.096154   14.165385                  21.815385   \n",
       "std     240.269886         127.875914   14.205998                  21.885199   \n",
       "min       0.000000           0.000000    0.000000                   0.000000   \n",
       "25%     210.000000          20.000000    2.375000                   3.750000   \n",
       "50%     340.000000         100.000000   11.000000                  17.000000   \n",
       "75%     500.000000         200.000000   22.250000                  35.000000   \n",
       "max    1880.000000        1060.000000  118.000000                 182.000000   \n",
       "\n",
       "       Saturated Fat  Saturated Fat (% Daily Value)   Trans Fat  Cholesterol  \\\n",
       "count     260.000000                     260.000000  260.000000   260.000000   \n",
       "mean        6.007692                      29.965385    0.203846    54.942308   \n",
       "std         5.321873                      26.639209    0.429133    87.269257   \n",
       "min         0.000000                       0.000000    0.000000     0.000000   \n",
       "25%         1.000000                       4.750000    0.000000     5.000000   \n",
       "50%         5.000000                      24.000000    0.000000    35.000000   \n",
       "75%        10.000000                      48.000000    0.000000    65.000000   \n",
       "max        20.000000                     102.000000    2.500000   575.000000   \n",
       "\n",
       "       Cholesterol (% Daily Value)       Sodium  ...  Carbohydrates  \\\n",
       "count                   260.000000   260.000000  ...     260.000000   \n",
       "mean                     18.392308   495.750000  ...      47.346154   \n",
       "std                      29.091653   577.026323  ...      28.252232   \n",
       "min                       0.000000     0.000000  ...       0.000000   \n",
       "25%                       2.000000   107.500000  ...      30.000000   \n",
       "50%                      11.000000   190.000000  ...      44.000000   \n",
       "75%                      21.250000   865.000000  ...      60.000000   \n",
       "max                     192.000000  3600.000000  ...     141.000000   \n",
       "\n",
       "       Carbohydrates (% Daily Value)  Dietary Fiber  \\\n",
       "count                     260.000000     260.000000   \n",
       "mean                       15.780769       1.630769   \n",
       "std                         9.419544       1.567717   \n",
       "min                         0.000000       0.000000   \n",
       "25%                        10.000000       0.000000   \n",
       "50%                        15.000000       1.000000   \n",
       "75%                        20.000000       3.000000   \n",
       "max                        47.000000       7.000000   \n",
       "\n",
       "       Dietary Fiber (% Daily Value)      Sugars     Protein  \\\n",
       "count                     260.000000  260.000000  260.000000   \n",
       "mean                        6.530769   29.423077   13.338462   \n",
       "std                         6.307057   28.679797   11.426146   \n",
       "min                         0.000000    0.000000    0.000000   \n",
       "25%                         0.000000    5.750000    4.000000   \n",
       "50%                         5.000000   17.500000   12.000000   \n",
       "75%                        10.000000   48.000000   19.000000   \n",
       "max                        28.000000  128.000000   87.000000   \n",
       "\n",
       "       Vitamin A (% Daily Value)  Vitamin C (% Daily Value)  \\\n",
       "count                 260.000000                 260.000000   \n",
       "mean                   13.426923                   8.534615   \n",
       "std                    24.366381                  26.345542   \n",
       "min                     0.000000                   0.000000   \n",
       "25%                     2.000000                   0.000000   \n",
       "50%                     8.000000                   0.000000   \n",
       "75%                    15.000000                   4.000000   \n",
       "max                   170.000000                 240.000000   \n",
       "\n",
       "       Calcium (% Daily Value)  Iron (% Daily Value)  \n",
       "count               260.000000            260.000000  \n",
       "mean                 20.973077              7.734615  \n",
       "std                  17.019953              8.723263  \n",
       "min                   0.000000              0.000000  \n",
       "25%                   6.000000              0.000000  \n",
       "50%                  20.000000              4.000000  \n",
       "75%                  30.000000             15.000000  \n",
       "max                  70.000000             40.000000  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "menu.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e21ad54",
   "metadata": {},
   "source": [
    "数据集不存在空值，开始进行下一步分析工作。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e443f3c3",
   "metadata": {},
   "source": [
    "## 探索性分析"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18c0432d",
   "metadata": {},
   "source": [
    "首先，我们看一下每一类食品的数量，并绘图展示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "80e9b8b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Coffee &amp; Tea</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Breakfast</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Smoothies &amp; Shakes</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beverages</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chicken &amp; Fish</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beef &amp; Pork</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Snacks &amp; Sides</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Desserts</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Salads</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Category  Counts\n",
       "4        Coffee & Tea      95\n",
       "2           Breakfast      42\n",
       "7  Smoothies & Shakes      28\n",
       "1           Beverages      27\n",
       "3      Chicken & Fish      27\n",
       "0         Beef & Pork      15\n",
       "8      Snacks & Sides      13\n",
       "5            Desserts       7\n",
       "6              Salads       6"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_by_category=menu[['Category']].groupby(['Category']).size().reset_index(name='Counts').sort_values(by='Counts',ascending=False)\n",
    "count_by_category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2f865408",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '每一类食品的数量')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(figsize=(12,9))\n",
    "ax=sns.barplot(x='Category',y='Counts',data=count_by_category)\n",
    "ax.set_title('每一类食品的数量')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dd594fde",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,9))\n",
    "plt.pie(x=count_by_category['Counts'],labels=count_by_category['Category'],autopct='%1.0f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afc82824",
   "metadata": {},
   "source": [
    "从条形图和饼形图可以看出，数量排名第一的品类是咖啡和茶，高达37%；之后是早餐（16%），冰沙奶昔（11%），饮品（10%），鸡肉鱼肉（10%）和牛肉猪肉（6%）。小吃，甜点和沙拉占比最少，其中沙拉类食品仅占所有食品的2%。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19741812",
   "metadata": {},
   "source": [
    "接下来看一下不同类型的食物，卡路里含量如何？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6b816f93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(figsize=(12,9))\n",
    "ax=sns.boxplot(x='Category',y='Calories',data=menu).set_title('卡路里')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29835ac0",
   "metadata": {},
   "source": [
    "#### 一些有趣的发现：\n",
    "\n",
    "早餐系列、猪肉牛肉系列、鸡肉鱼肉系列的卡路里含量较高（主食），冰沙奶昔系列的卡路里含量最高；\n",
    "沙拉、小食、甜品、咖啡和茶的卡路里含量较低，饮品的卡路里含量最低；\n",
    "早餐系列、鸡肉鱼肉系列和咖啡茶系列有一些异常值(outlier)，可能是一些大份食物。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0905d92a",
   "metadata": {},
   "source": [
    "以上分析提醒了我们，食品的卡路里含量（及其他成分含量）会受到其份量的影响，将食品调整至同样的份量可以让之后的分析更加客观。\n",
    "\n",
    "通过回顾数据集中“Serving Size”一栏，我们发现麦当劳对份量的标注并不完全统一，有如下几种形式：\n",
    "\n",
    "    1）4.8 oz (136 g) \n",
    "    2）1 cookie (33 g) \n",
    "    3）21 fl oz cup \n",
    "    4) 1 carton (236 ml) \n",
    "    5) 6 fl oz (177 ml) \n",
    "    6) 16.9 fl oz\n",
    "\n",
    "固体食物的份量标注比较统一，都是按1）的形式，标出了oz和g两种重量单位，唯一的特例是2），只标注了g这一重量单位。\n",
    "\n",
    "半液态和液态食物的份量标注比较杂乱，有些和固体食物一样标注了重量，但大部分属于3）-6），即标注fl oz和/或ml两种体积单位。\n",
    "\n",
    "因此我们的提取原则是，重量单位优先提取g，没有g再提取oz，并转换为g；体积单位优先提取ml，没有ml再提取fl oz，并转换为ml。\n",
    "\n",
    "*注：1盎司(oz)=28.35克(g)；1美制液体盎司(fl oz)=29.57毫升(ml)\n",
    "\n",
    "因为重量单位和体积单位的不一致，为了之后的分析方便，我们假设1ml的液体等于1g，并把一些半液态和液态食品的份量从体积转为重量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fef3c9d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Item</th>\n",
       "      <th>Serving Size</th>\n",
       "      <th>Calories</th>\n",
       "      <th>Calories from Fat</th>\n",
       "      <th>Total Fat</th>\n",
       "      <th>Total Fat (% Daily Value)</th>\n",
       "      <th>Saturated Fat</th>\n",
       "      <th>Saturated Fat (% Daily Value)</th>\n",
       "      <th>Trans Fat</th>\n",
       "      <th>...</th>\n",
       "      <th>Dietary Fiber</th>\n",
       "      <th>Dietary Fiber (% Daily Value)</th>\n",
       "      <th>Sugars</th>\n",
       "      <th>Protein</th>\n",
       "      <th>Vitamin A (% Daily Value)</th>\n",
       "      <th>Vitamin C (% Daily Value)</th>\n",
       "      <th>Calcium (% Daily Value)</th>\n",
       "      <th>Iron (% Daily Value)</th>\n",
       "      <th>Size g</th>\n",
       "      <th>Calories per 100g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Breakfast</td>\n",
       "      <td>Egg McMuffin</td>\n",
       "      <td>4.8 oz (136 g)</td>\n",
       "      <td>300</td>\n",
       "      <td>120</td>\n",
       "      <td>13.0</td>\n",
       "      <td>20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "      <td>136.0</td>\n",
       "      <td>220.588235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Breakfast</td>\n",
       "      <td>Egg White Delight</td>\n",
       "      <td>4.8 oz (135 g)</td>\n",
       "      <td>250</td>\n",
       "      <td>70</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>135.0</td>\n",
       "      <td>185.185185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Breakfast</td>\n",
       "      <td>Sausage McMuffin</td>\n",
       "      <td>3.9 oz (111 g)</td>\n",
       "      <td>370</td>\n",
       "      <td>200</td>\n",
       "      <td>23.0</td>\n",
       "      <td>35</td>\n",
       "      <td>8.0</td>\n",
       "      <td>42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "      <td>111.0</td>\n",
       "      <td>333.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Breakfast</td>\n",
       "      <td>Sausage McMuffin with Egg</td>\n",
       "      <td>5.7 oz (161 g)</td>\n",
       "      <td>450</td>\n",
       "      <td>250</td>\n",
       "      <td>28.0</td>\n",
       "      <td>43</td>\n",
       "      <td>10.0</td>\n",
       "      <td>52</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>161.0</td>\n",
       "      <td>279.503106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Breakfast</td>\n",
       "      <td>Sausage McMuffin with Egg Whites</td>\n",
       "      <td>5.7 oz (161 g)</td>\n",
       "      <td>400</td>\n",
       "      <td>210</td>\n",
       "      <td>23.0</td>\n",
       "      <td>35</td>\n",
       "      <td>8.0</td>\n",
       "      <td>42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>10</td>\n",
       "      <td>161.0</td>\n",
       "      <td>248.447205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Category                              Item    Serving Size  Calories  \\\n",
       "0  Breakfast                      Egg McMuffin  4.8 oz (136 g)       300   \n",
       "1  Breakfast                 Egg White Delight  4.8 oz (135 g)       250   \n",
       "2  Breakfast                  Sausage McMuffin  3.9 oz (111 g)       370   \n",
       "3  Breakfast         Sausage McMuffin with Egg  5.7 oz (161 g)       450   \n",
       "4  Breakfast  Sausage McMuffin with Egg Whites  5.7 oz (161 g)       400   \n",
       "\n",
       "   Calories from Fat  Total Fat  Total Fat (% Daily Value)  Saturated Fat  \\\n",
       "0                120       13.0                         20            5.0   \n",
       "1                 70        8.0                         12            3.0   \n",
       "2                200       23.0                         35            8.0   \n",
       "3                250       28.0                         43           10.0   \n",
       "4                210       23.0                         35            8.0   \n",
       "\n",
       "   Saturated Fat (% Daily Value)  Trans Fat  ...  Dietary Fiber  \\\n",
       "0                             25        0.0  ...              4   \n",
       "1                             15        0.0  ...              4   \n",
       "2                             42        0.0  ...              4   \n",
       "3                             52        0.0  ...              4   \n",
       "4                             42        0.0  ...              4   \n",
       "\n",
       "   Dietary Fiber (% Daily Value)  Sugars  Protein  Vitamin A (% Daily Value)  \\\n",
       "0                             17       3       17                         10   \n",
       "1                             17       3       18                          6   \n",
       "2                             17       2       14                          8   \n",
       "3                             17       2       21                         15   \n",
       "4                             17       2       21                          6   \n",
       "\n",
       "   Vitamin C (% Daily Value)  Calcium (% Daily Value)  Iron (% Daily Value)  \\\n",
       "0                          0                       25                    15   \n",
       "1                          0                       25                     8   \n",
       "2                          0                       25                    10   \n",
       "3                          0                       30                    15   \n",
       "4                          0                       25                    10   \n",
       "\n",
       "   Size g  Calories per 100g  \n",
       "0   136.0         220.588235  \n",
       "1   135.0         185.185185  \n",
       "2   111.0         333.333333  \n",
       "3   161.0         279.503106  \n",
       "4   161.0         248.447205  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#正则表达式匹配几种不同的份量标注方式，\n",
    "#re.compile是将正则表达式编译成一个对象，加快速度，并重复使用。\n",
    "# []方括号在正则表达式中用于单个匹配，可用于字符集、排除字符集之中。\n",
    "import re\n",
    "p1 = re.compile(r'[(](.*?)[ g)]')\n",
    "p2 = re.compile(r'(.*?)[ ]') \n",
    "\n",
    "def getLambda(x,p1,p2):\n",
    "    try:\n",
    "        val=float(re.findall(p1,x)[0])\n",
    "    except:\n",
    "        val=float(re.findall(p2,x)[0])*29.57# 提取的是fl oz，乘以29.57转换为ml\n",
    "    return val\n",
    "\n",
    "norm_menu=menu.iloc[:,:]\n",
    "norm_menu['Size g']=norm_menu['Serving Size'].apply(lambda x:getLambda(x,p1,p2))\n",
    "norm_menu['Calories per 100g']=(norm_menu['Calories']/norm_menu['Size g'])*100\n",
    "\n",
    "norm_menu.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "731e5ef1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '统一重量单位之后的卡路里')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(figsize=(12,9))\n",
    "sns.boxplot(x='Category',y='Calories per 100g',data=norm_menu).set_title('统一重量单位之后的卡路里')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c40d2ef7",
   "metadata": {},
   "source": [
    "对比没有调整过的数据画出的盒形图可以得知：\n",
    "\n",
    "固态食品中，主食（早餐，牛肉猪肉，鸡肉鱼肉）依旧是卡路里含量最高的食品。之前我们以为卡路里含量较低的小食、甜点等，其实只是因为份量较小，如果换算成同样的份量，卡路里含量也不低，和主食持平。只有沙拉的卡路里含量远低于其他类食品，其平均值是主食的一半左右。\n",
    "\n",
    "液体及半液体的饮品，卡路里含量总体低于固体食品，但冰沙奶昔的卡路里明显高于饮料和咖啡茶，甚至高于沙拉。\n",
    "\n",
    "因此，对于一个选择麦当劳就餐的减肥人士来说，为了填饱肚子，最好点一份沙拉。如果还想点一份饮品，不建议点冰沙奶昔。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c560b2ec",
   "metadata": {},
   "source": [
    "## 通过轮廓图和相关图来比较特征"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37f922c3",
   "metadata": {},
   "source": [
    "#### 轮廓图\n",
    "\n",
    "我们首先看看一个特征如何影响其他特征：轮廓图或者KDE图可以提供一个特征相对于另一个特征的分布。简单来讲，这让我们对定量数据有一个快速的感知。调用了Seaborn中的kdeplot函数。我们选取了几个主要特征，并生成了9张轮廓图，如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9f6b453a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# KDE图\n",
    "f, axes = plt.subplots(3, 3, figsize=(10, 10), sharex=True, sharey=True)\n",
    "\n",
    "s = np.linspace(0, 3, 10)\n",
    "cmap = sns.cubehelix_palette(start=0.0, light=1, as_cmap=True)\n",
    "\n",
    "# Generate and plot a random bivariate dataset\n",
    "x = menu['Cholesterol (% Daily Value)'].values\n",
    "y = menu['Sodium (% Daily Value)'].values\n",
    "sns.kdeplot(x, y, cmap=cmap, shade=True, cut=5, ax=axes[0,0])\n",
    "axes[0,0].set(xlim=(-10, 50), ylim=(-30, 70), title = 'Cholesterol and Sodium')\n",
    "\n",
    "cmap = sns.cubehelix_palette(start=0.333333333333, light=1, as_cmap=True)\n",
    "\n",
    "# Generate and plot a random bivariate dataset\n",
    "x = menu['Carbohydrates (% Daily Value)'].values\n",
    "y = menu['Sodium (% Daily Value)'].values\n",
    "sns.kdeplot(x, y, cmap=cmap, shade=True, ax=axes[0,1])\n",
    "axes[0,1].set(xlim=(-5, 50), ylim=(-10, 70),  title = 'Carbs and Sodium')\n",
    "\n",
    "cmap = sns.cubehelix_palette(start=0.666666666667, light=1, as_cmap=True)\n",
    "\n",
    "# Generate and plot a random bivariate dataset\n",
    "x = menu['Carbohydrates (% Daily Value)'].values\n",
    "y = menu['Cholesterol (% Daily Value)'].values\n",
    "sns.kdeplot(x, y, cmap=cmap, shade=True, ax=axes[0,2])\n",
    "axes[0,2].set(xlim=(-5, 50), ylim=(-10, 70),  title = 'Carbs and Cholesterol')\n",
    "\n",
    "cmap = sns.cubehelix_palette(start=1.0, light=1, as_cmap=True)\n",
    "\n",
    "# Generate and plot a random bivariate dataset\n",
    "x = menu['Total Fat (% Daily Value)'].values\n",
    "y = menu['Saturated Fat (% Daily Value)'].values\n",
    "sns.kdeplot(x, y, cmap=cmap, shade=True, ax=axes[1,0])\n",
    "axes[1,0].set(xlim=(-5, 50), ylim=(-10, 70),  title = 'Total Fat and Saturated Fat')\n",
    "\n",
    "cmap = sns.cubehelix_palette(start=1.333333333333, light=1, as_cmap=True)\n",
    "\n",
    "# Generate and plot a random bivariate dataset\n",
    "x = menu['Total Fat (% Daily Value)'].values\n",
    "y = menu['Cholesterol (% Daily Value)'].values\n",
    "sns.kdeplot(x, y, cmap=cmap, shade=True, ax=axes[1,1])\n",
    "axes[1,1].set(xlim=(-5, 50), ylim=(-10, 70),  title = 'Cholesterol and Total Fat')\n",
    "\n",
    "cmap = sns.cubehelix_palette(start=1.666666666667, light=1, as_cmap=True)\n",
    "\n",
    "# Generate and plot a random bivariate dataset\n",
    "x = menu['Vitamin A (% Daily Value)'].values\n",
    "y = menu['Cholesterol (% Daily Value)'].values\n",
    "sns.kdeplot(x, y, cmap=cmap, shade=True, ax=axes[1,2])\n",
    "axes[1,2].set(xlim=(-5, 50), ylim=(-10, 70),  title = 'Vitamin A and Cholesterol')\n",
    "\n",
    "cmap = sns.cubehelix_palette(start=2.0, light=1, as_cmap=True)\n",
    "\n",
    "# Generate and plot a random bivariate dataset\n",
    "x = menu['Calcium (% Daily Value)'].values\n",
    "y = menu['Sodium (% Daily Value)'].values\n",
    "sns.kdeplot(x, y, cmap=cmap, shade=True, ax=axes[2,0])\n",
    "axes[2,0].set(xlim=(-5, 50), ylim=(-10, 70),  title = 'Calcium and Sodium')\n",
    "\n",
    "cmap = sns.cubehelix_palette(start=2.333333333333, light=1, as_cmap=True)\n",
    "\n",
    "# Generate and plot a random bivariate dataset\n",
    "x = menu['Calcium (% Daily Value)'].values\n",
    "y = menu['Cholesterol (% Daily Value)'].values\n",
    "sns.kdeplot(x, y, cmap=cmap, shade=True, ax=axes[2,1])\n",
    "axes[2,1].set(xlim=(-5, 50), ylim=(-10, 70),  title = 'Cholesterol and Calcium')\n",
    "\n",
    "cmap = sns.cubehelix_palette(start=2.666666666667, light=1, as_cmap=True)\n",
    "\n",
    "# Generate and plot a random bivariate dataset\n",
    "x = menu['Iron (% Daily Value)'].values\n",
    "y = menu['Total Fat (% Daily Value)'].values\n",
    "sns.kdeplot(x, y, cmap=cmap, shade=True, ax=axes[2,2])\n",
    "axes[2,2].set(xlim=(-5, 50), ylim=(-10, 70),  title = 'Iron and Total Fat')\n",
    "\n",
    "\n",
    "f.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db11d6fa",
   "metadata": {},
   "source": [
    "#### Pearson相关图\n",
    "\n",
    "现在绘制Pearson相关图，检查不同营养指标之间的相关程度。这次，我们调用了Plotly的交互式绘图功能，绘制特征之间相关性的热图（Heatmap），如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "787926e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plotly.com",
        "plotlyServerURL": "https://plotly.com",
        "showLink": false
       },
       "data": [
        {
         "colorscale": [
          [
           0,
           "#440154"
          ],
          [
           0.1111111111111111,
           "#482878"
          ],
          [
           0.2222222222222222,
           "#3e4989"
          ],
          [
           0.3333333333333333,
           "#31688e"
          ],
          [
           0.4444444444444444,
           "#26828e"
          ],
          [
           0.5555555555555556,
           "#1f9e89"
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                       {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('e9c9adc3-9568-4b4f-9ed6-a7300befc03c');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "\n",
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       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [\n",
    "    go.Heatmap(\n",
    "        z = menu.iloc[:, 3:].corr().values,\n",
    "        x = menu.columns.values,\n",
    "        y = menu.columns.values,\n",
    "        colorscale = 'Viridis',\n",
    "#         text = True,\n",
    "        opacity = 1.0\n",
    "    )\n",
    "]\n",
    "\n",
    "layout = go.Layout(\n",
    "    title = '各个营养指标的Pearson相关图',\n",
    "    xaxis = dict(ticks='', nticks=36),\n",
    "    yaxis = dict(ticks=''),\n",
    "    width = 900, height = 700,\n",
    ")\n",
    "\n",
    "fig = go.Figure(data = data, layout = layout)\n",
    "py.iplot(fig, filename = 'labelled-heatmap')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8c47037",
   "metadata": {},
   "source": [
    "#### 点击不同的方格，可以查看某两个特征的相关性。颜色越趋近于黄色，说明两者正相关性越强。颜色越趋近于紫色，说明两者负相关性越强。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b369e30",
   "metadata": {},
   "source": [
    "    从相关图中可以看出明显相关的特征，例如份量和卡路里的相关性高达0.9。\n",
    "    然而，有一些相关性非常不直观。例如，总脂肪和饱和脂肪/反式脂肪之间存在相当弱的相关性，但在我们普通人的认知中，这两者理应早存一定相关性。\n",
    "\n",
    "    热图也从负相关图（深蓝/黑）的斑点中引出了有趣的发现。例如，它表明碳水化合物通常与反式脂肪，胆固醇，钠，膳食纤维和维生素A呈负相关。这与碳水化合物的负相关性确实很多。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "936a1091",
   "metadata": {},
   "source": [
    "#### 数据质量是否存在任何问题？\n",
    "\n",
    "现在很明显，碳水化合物列与其他列负相关程度很强，这是符合常识的。然而，或许存在如下可能性：含碳水化合物的食物可能除了碳水化合物之外没有其他东西，从而导致了上面提到的负相关性，这也是需要在分析中结合实际情况思考的。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47311e2c",
   "metadata": {},
   "source": [
    "## 分析每个产品的营养成分"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ad73ac4",
   "metadata": {},
   "source": [
    "对数据集中的特征进行了高度概述后，我们接下来进行更细粒度的分析。\n",
    "\n",
    "首先，我们要解释一下Daily Value（DV，每日推荐摄入量）的概念。它由FDA规定，是一份食物中关于营养成分的指南，基于一个健康成年人每天食用2000卡路里的食品。例如，标签中标明15%的钙，那说明一份食物可以提供你一天所需要的钙的15%。\n",
    "\n",
    "DV可以告诉你一种食品的营养成分是高还是低，如果在5%以下，就是偏低的；如果在20%以上，就是偏高的。对于Trans Fat，FDA没有规定DV数值，因为专家推荐人们尽量避免含有反式脂肪的食品。总体来看，应该选择富含维生素、矿物质和膳食纤维的食品，避免过多含有饱和脂肪酸、糖和纳的食品。\n",
    "\n",
    "相比与营养成分本身的数值，其占每日推荐摄入量的百分比显得更加直观，因此接下来的分析使用的都是百分比。我们使用Plotly交互式可视化工具，分析每一个产品的营养成分，并绘制交互式散点图。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ab3d141",
   "metadata": {},
   "source": [
    "### 每个麦当劳产品的胆固醇（占每日推荐摄入量的百分比）散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a8e03146",
   "metadata": {},
   "outputs": [
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          "Sausage McMuffin with Egg",
          "Sausage McMuffin with Egg Whites",
          "Steak & Egg McMuffin",
          "Bacon, Egg & Cheese Biscuit (Regular Biscuit)",
          "Bacon, Egg & Cheese Biscuit (Large Biscuit)",
          "Bacon, Egg & Cheese Biscuit with Egg Whites (Regular Biscuit)",
          "Bacon, Egg & Cheese Biscuit with Egg Whites (Large Biscuit)",
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          "Sausage Biscuit (Large Biscuit)",
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          "Sausage Biscuit with Egg (Large Biscuit)",
          "Sausage Biscuit with Egg Whites (Regular Biscuit)",
          "Sausage Biscuit with Egg Whites (Large Biscuit)",
          "Southern Style Chicken Biscuit (Regular Biscuit)",
          "Southern Style Chicken Biscuit (Large Biscuit)",
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          "Nonfat Caramel Latte (Medium)",
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          "Nonfat Hazelnut Latte (Large)",
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          "Frappé Chocolate Chip (Large)",
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          "Chocolate Shake (Small)",
          "Chocolate Shake (Medium)",
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          "McFlurry with Oreo Cookies (Snack)",
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          "Steak & Egg McMuffin",
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(% Daily Value)\"},\"zeroline\":false}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('67d2edc9-6147-4afb-bd94-5ae2d7d51521');\n",
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trace = go.Scatter(\n",
    "    y = menu['Cholesterol (% Daily Value)'].values,\n",
    "    x = menu['Item'].values,\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        size= menu['Cholesterol (% Daily Value)'].values,\n",
    "        color = menu['Cholesterol (% Daily Value)'].values,\n",
    "        colorscale='Portland',\n",
    "        showscale=True\n",
    "    ),\n",
    "    text = menu['Item'].values\n",
    ")\n",
    "data = [trace]\n",
    "\n",
    "layout= go.Layout(\n",
    "    autosize= True,\n",
    "    title= '胆固醇（占每日推荐摄入量的百分比）',\n",
    "    hovermode= 'closest',\n",
    "    xaxis=dict(\n",
    "        showgrid=False,\n",
    "        zeroline=False,\n",
    "        showline=False\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        title= 'Cholesterol (% Daily Value)',\n",
    "        ticklen= 5,\n",
    "        gridwidth= 2,\n",
    "        showgrid=False,\n",
    "        zeroline=False,\n",
    "        showline=False\n",
    "    ),\n",
    "    showlegend= False\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig,filename='scatterChol')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5377a16",
   "metadata": {},
   "source": [
    "一些有趣的发现：\n",
    "\n",
    "胆固醇散点图（以及之后一系列2D散点图）中，点的大小和颜色与营养成分的含量有关，十分直观。\n",
    "\n",
    "最引人注目的是位于左上方的几个红点（Big Breakfast系列），这表明某些食品的胆固醇含量远超每日推荐摄入量，甚至达到了185%。红点下面一些是更多的黄点（如Egg/Sausage麦满分系列），这些食品的胆固醇含量基本上也满足了每日摄入量。\n",
    "\n",
    "含有胆固醇最多的食物：Big Breakfast (Large Biscuit)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "230e7c91",
   "metadata": {},
   "source": [
    "### 每个麦当劳产品的纳（占每日推荐摄入量的百分比）散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "de25d4a5",
   "metadata": {},
   "outputs": [
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         "text": [
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          "Sausage McMuffin with Egg",
          "Sausage McMuffin with Egg Whites",
          "Steak & Egg McMuffin",
          "Bacon, Egg & Cheese Biscuit (Regular Biscuit)",
          "Bacon, Egg & Cheese Biscuit (Large Biscuit)",
          "Bacon, Egg & Cheese Biscuit with Egg Whites (Regular Biscuit)",
          "Bacon, Egg & Cheese Biscuit with Egg Whites (Large Biscuit)",
          "Sausage Biscuit (Regular Biscuit)",
          "Sausage Biscuit (Large Biscuit)",
          "Sausage Biscuit with Egg (Regular Biscuit)",
          "Sausage Biscuit with Egg (Large Biscuit)",
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          "Sausage Biscuit with Egg Whites (Large Biscuit)",
          "Southern Style Chicken Biscuit (Regular Biscuit)",
          "Southern Style Chicken Biscuit (Large Biscuit)",
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          "Iced Coffee with Sugar Free French Vanilla Syrup (Medium)",
          "Iced Coffee with Sugar Free French Vanilla Syrup (Large)",
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          "Frappé Caramel (Large)",
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          "Chocolate Shake (Large)",
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(% Daily Value)\"},\"zeroline\":false}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('a365d1b4-1b10-44d8-8567-9c5e4f3d96fc');\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trace = go.Scatter(\n",
    "    y = menu['Sodium (% Daily Value)'].values,\n",
    "    x = menu['Item'].values,\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        size= menu['Sodium (% Daily Value)'].values,\n",
    "        color = menu['Sodium (% Daily Value)'].values,\n",
    "        colorscale='Portland',\n",
    "        showscale=True\n",
    "    ),\n",
    "    text = menu['Item'].values\n",
    ")\n",
    "data = [trace]\n",
    " \n",
    "layout= go.Layout(\n",
    "    autosize= True,\n",
    "    title= '纳（占每日推荐摄入量的百分比）',\n",
    "    hovermode= 'closest',\n",
    "        xaxis=dict(\n",
    "        showgrid=False,\n",
    "        zeroline=False,\n",
    "        showline=False\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        title= 'Sodium (% Daily Value)',\n",
    "        ticklen= 5,\n",
    "        gridwidth= 2,\n",
    "        showgrid=False,\n",
    "        zeroline=False,\n",
    "        showline=False,\n",
    "    ),\n",
    "    showlegend= False\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig,filename='scatterChol')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1772a9a",
   "metadata": {},
   "source": [
    "#### 一些有趣的发现：\n",
    "\n",
    "纳散点图的分布与上面的胆固醇散点图分布比较类似。最大的红点来自于麦乐鸡（含40块），其含钠量是每日推荐摄入量的150%，其次是大份早餐拼盘（含松饼）系列，在90%左右。\n",
    "\n",
    "含有纳最多的食物：麦乐鸡（含40块）"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32f792b9",
   "metadata": {},
   "source": [
    "### 每个麦当劳产品的总脂肪和碳水化合物水平的3D散点图\n",
    "\n",
    "我们已经画了一些2D散点图，现在来探索一下Plotly的3D散点图功能。3D相对于2D加入了食品类别（Category）这个维度。\n",
    "\n",
    "以下两幅3D散点图绘制了麦当劳不同产品的碳水化合物和总脂肪含量的分布情况：x轴是产品类别，y轴是具体产品，z轴是营养成分含量。\n",
    "\n",
    "Plotly让我们能够很轻松的旋转3D图，查看坐标数据，从而获取更多直观的数据信息。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9d034a80",
   "metadata": {},
   "outputs": [
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          "Cinnamon Melts",
          "Fruit & Maple Oatmeal",
          "Fruit & Maple Oatmeal without Brown Sugar",
          "Big Mac",
          "Quarter Pounder with Cheese",
          "Quarter Pounder with Bacon & Cheese",
          "Quarter Pounder with Bacon Habanero Ranch",
          "Quarter Pounder Deluxe",
          "Double Quarter Pounder with Cheese",
          "Hamburger",
          "Cheeseburger",
          "Double Cheeseburger",
          "Bacon Clubhouse Burger",
          "McDouble",
          "Bacon McDouble",
          "Daily Double",
          "Jalapeño Double",
          "McRib",
          "Premium Crispy Chicken Classic Sandwich",
          "Premium Grilled Chicken Classic Sandwich",
          "Premium Crispy Chicken Club Sandwich",
          "Premium Grilled Chicken Club Sandwich",
          "Premium Crispy Chicken Ranch BLT Sandwich",
          "Premium Grilled Chicken Ranch BLT Sandwich",
          "Bacon Clubhouse Crispy Chicken Sandwich",
          "Bacon Clubhouse Grilled Chicken Sandwich",
          "Southern Style Crispy Chicken Sandwich",
          "McChicken",
          "Bacon Cheddar McChicken",
          "Bacon Buffalo Ranch McChicken",
          "Buffalo Ranch McChicken",
          "Premium McWrap Chicken & Bacon (Crispy Chicken)",
          "Premium McWrap Chicken & Bacon (Grilled Chicken)",
          "Premium McWrap Chicken & Ranch (Crispy Chicken)",
          "Premium McWrap Chicken & Ranch (Grilled Chicken)",
          "Premium McWrap Southwest Chicken (Crispy Chicken)",
          "Premium McWrap Southwest Chicken (Grilled Chicken)",
          "Premium McWrap Chicken Sweet Chili (Crispy Chicken)",
          "Premium McWrap Chicken Sweet Chili (Grilled Chicken)",
          "Chicken McNuggets (4 piece)",
          "Chicken McNuggets (6 piece)",
          "Chicken McNuggets (10 piece)",
          "Chicken McNuggets (20 piece)",
          "Chicken McNuggets (40 piece)",
          "Filet-O-Fish",
          "Premium Bacon Ranch Salad (without Chicken)",
          "Premium Bacon Ranch Salad with Crispy Chicken",
          "Premium Bacon Ranch Salad with Grilled Chicken",
          "Premium Southwest Salad (without Chicken)",
          "Premium Southwest Salad with Crispy Chicken",
          "Premium Southwest Salad with Grilled Chicken",
          "Chipotle BBQ Snack Wrap (Crispy Chicken)",
          "Chipotle BBQ Snack Wrap (Grilled Chicken)",
          "Honey Mustard Snack Wrap (Crispy Chicken)",
          "Honey Mustard Snack Wrap (Grilled Chicken)",
          "Ranch Snack Wrap (Crispy Chicken)",
          "Ranch Snack Wrap (Grilled Chicken)",
          "Small French Fries",
          "Medium French Fries",
          "Large French Fries",
          "Kids French Fries",
          "Side Salad",
          "Apple Slices",
          "Fruit 'n Yogurt Parfait",
          "Baked Apple Pie",
          "Chocolate Chip Cookie",
          "Oatmeal Raisin Cookie",
          "Kids Ice Cream Cone",
          "Hot Fudge Sundae",
          "Hot Caramel Sundae",
          "Strawberry Sundae",
          "Coca-Cola Classic (Small)",
          "Coca-Cola Classic (Medium)",
          "Coca-Cola Classic (Large)",
          "Coca-Cola Classic (Child)",
          "Diet Coke (Small)",
          "Diet Coke (Medium)",
          "Diet Coke (Large)",
          "Diet Coke (Child)",
          "Dr Pepper (Small)",
          "Dr Pepper (Medium)",
          "Dr Pepper (Large)",
          "Dr Pepper (Child)",
          "Diet Dr Pepper (Small)",
          "Diet Dr Pepper (Medium)",
          "Diet Dr Pepper (Large)",
          "Diet Dr Pepper (Child)",
          "Sprite (Small)",
          "Sprite (Medium)",
          "Sprite (Large)",
          "Sprite (Child)",
          "1% Low Fat Milk Jug",
          "Fat Free Chocolate Milk Jug",
          "Minute Maid 100% Apple Juice Box",
          "Minute Maid Orange Juice (Small)",
          "Minute Maid Orange Juice (Medium)",
          "Minute Maid Orange Juice (Large)",
          "Dasani Water Bottle",
          "Iced Tea (Small)",
          "Iced Tea (Medium)",
          "Iced Tea (Large)",
          "Iced Tea (Child)",
          "Sweet Tea (Small)",
          "Sweet Tea (Medium)",
          "Sweet Tea (Large)",
          "Sweet Tea (Child)",
          "Coffee (Small)",
          "Coffee (Medium)",
          "Coffee (Large)",
          "Latte (Small)",
          "Latte (Medium)",
          "Latte (Large)",
          "Caramel Latte (Small)",
          "Caramel Latte (Medium)",
          "Caramel Latte (Large)",
          "Hazelnut Latte (Small)",
          "Hazelnut Latte (Medium)",
          "Hazelnut Latte (Large)",
          "French Vanilla Latte (Small)",
          "French Vanilla Latte (Medium)",
          "French Vanilla Latte (Large)",
          "Latte with Sugar Free French Vanilla Syrup (Small)",
          "Latte with Sugar Free French Vanilla Syrup (Medium)",
          "Latte with Sugar Free French Vanilla Syrup (Large)",
          "Nonfat Latte (Small)",
          "Nonfat Latte (Medium)",
          "Nonfat Latte (Large)",
          "Nonfat Caramel Latte (Small)",
          "Nonfat Caramel Latte (Medium)",
          "Nonfat Caramel Latte (Large)",
          "Nonfat Hazelnut Latte (Small)",
          "Nonfat Hazelnut Latte (Medium)",
          "Nonfat Hazelnut Latte (Large)",
          "Nonfat French Vanilla Latte (Small)",
          "Nonfat French Vanilla Latte (Medium)",
          "Nonfat French Vanilla Latte (Large)",
          "Nonfat Latte with Sugar Free French Vanilla Syrup (Small)",
          "Nonfat Latte with Sugar Free French Vanilla Syrup (Medium)",
          "Nonfat Latte with Sugar Free French Vanilla Syrup (Large)",
          "Mocha (Small)",
          "Mocha (Medium)",
          "Mocha (Large)",
          "Mocha with Nonfat Milk (Small)",
          "Mocha with Nonfat Milk (Medium)",
          "Mocha with Nonfat Milk (Large)",
          "Caramel Mocha (Small)",
          "Caramel Mocha (Medium)",
          "Caramel Mocha (Large)",
          "Nonfat Caramel Mocha (Small)",
          "Nonfat Caramel Mocha (Medium)",
          "Nonfat Caramel Mocha (Large)",
          "Hot Chocolate (Small)",
          "Hot Chocolate (Medium)",
          "Hot Chocolate (Large)",
          "Hot Chocolate with Nonfat Milk (Small)",
          "Hot Chocolate with Nonfat Milk (Medium)",
          "Hot Chocolate with Nonfat Milk (Large)",
          "Regular Iced Coffee (Small)",
          "Regular Iced Coffee (Medium)",
          "Regular Iced Coffee (Large)",
          "Caramel Iced Coffee (Small)",
          "Caramel Iced Coffee (Medium)",
          "Caramel Iced Coffee (Large)",
          "Hazelnut Iced Coffee (Small)",
          "Hazelnut Iced Coffee (Medium)",
          "Hazelnut Iced Coffee (Large)",
          "French Vanilla Iced Coffee (Small)",
          "French Vanilla Iced Coffee (Medium)",
          "French Vanilla Iced Coffee (Large)",
          "Iced Coffee with Sugar Free French Vanilla Syrup (Small)",
          "Iced Coffee with Sugar Free French Vanilla Syrup (Medium)",
          "Iced Coffee with Sugar Free French Vanilla Syrup (Large)",
          "Iced Mocha (Small)",
          "Iced Mocha (Medium)",
          "Iced Mocha (Large)",
          "Iced Mocha with Nonfat Milk (Small)",
          "Iced Mocha with Nonfat Milk (Medium)",
          "Iced Mocha with Nonfat Milk (Large)",
          "Iced Caramel Mocha (Small)",
          "Iced Caramel Mocha (Medium)",
          "Iced Caramel Mocha (Large)",
          "Iced Nonfat Caramel Mocha (Small)",
          "Iced Nonfat Caramel Mocha (Medium)",
          "Iced Nonfat Caramel Mocha (Large)",
          "Frappé Mocha (Small)",
          "Frappé Mocha (Medium)",
          "Frappé Mocha (Large)",
          "Frappé Caramel (Small)",
          "Frappé Caramel (Medium)",
          "Frappé Caramel (Large)",
          "Frappé Chocolate Chip (Small)",
          "Frappé Chocolate Chip (Medium)",
          "Frappé Chocolate Chip (Large)",
          "Blueberry Pomegranate Smoothie (Small)",
          "Blueberry Pomegranate Smoothie (Medium)",
          "Blueberry Pomegranate Smoothie (Large)",
          "Strawberry Banana Smoothie (Small)",
          "Strawberry Banana Smoothie (Medium)",
          "Strawberry Banana Smoothie (Large)",
          "Mango Pineapple Smoothie (Small)",
          "Mango Pineapple Smoothie (Medium)",
          "Mango Pineapple Smoothie (Large)",
          "Vanilla Shake (Small)",
          "Vanilla Shake (Medium)",
          "Vanilla Shake (Large)",
          "Strawberry Shake (Small)",
          "Strawberry Shake (Medium)",
          "Strawberry Shake (Large)",
          "Chocolate Shake (Small)",
          "Chocolate Shake (Medium)",
          "Chocolate Shake (Large)",
          "Shamrock Shake (Medium)",
          "Shamrock Shake (Large)",
          "McFlurry with M&M’s Candies (Small)",
          "McFlurry with M&M’s Candies (Medium)",
          "McFlurry with M&M’s Candies (Snack)",
          "McFlurry with Oreo Cookies (Small)",
          "McFlurry with Oreo Cookies (Medium)",
          "McFlurry with Oreo Cookies (Snack)",
          "McFlurry with Reese's Peanut Butter Cups (Medium)",
          "McFlurry with Reese's Peanut Butter Cups (Snack)"
         ],
         "type": "scatter3d",
         "x": [
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Beef & Pork",
          "Beef & Pork",
          "Beef & Pork",
          "Beef & Pork",
          "Beef & Pork",
          "Beef & Pork",
          "Beef & Pork",
          "Beef & Pork",
          "Beef & Pork",
          "Beef & Pork",
          "Beef & Pork",
          "Beef & Pork",
          "Beef & Pork",
          "Beef & Pork",
          "Beef & Pork",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Salads",
          "Salads",
          "Salads",
          "Salads",
          "Salads",
          "Salads",
          "Snacks & Sides",
          "Snacks & Sides",
          "Snacks & Sides",
          "Snacks & Sides",
          "Snacks & Sides",
          "Snacks & Sides",
          "Snacks & Sides",
          "Snacks & Sides",
          "Snacks & Sides",
          "Snacks & Sides",
          "Snacks & Sides",
          "Snacks & Sides",
          "Snacks & Sides",
          "Desserts",
          "Desserts",
          "Desserts",
          "Desserts",
          "Desserts",
          "Desserts",
          "Desserts",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes"
         ],
         "y": [
          "Egg McMuffin",
          "Egg White Delight",
          "Sausage McMuffin",
          "Sausage McMuffin with Egg",
          "Sausage McMuffin with Egg Whites",
          "Steak & Egg McMuffin",
          "Bacon, Egg & Cheese Biscuit (Regular Biscuit)",
          "Bacon, Egg & Cheese Biscuit (Large Biscuit)",
          "Bacon, Egg & Cheese Biscuit with Egg Whites (Regular Biscuit)",
          "Bacon, Egg & Cheese Biscuit with Egg Whites (Large Biscuit)",
          "Sausage Biscuit (Regular Biscuit)",
          "Sausage Biscuit (Large Biscuit)",
          "Sausage Biscuit with Egg (Regular Biscuit)",
          "Sausage Biscuit with Egg (Large Biscuit)",
          "Sausage Biscuit with Egg Whites (Regular Biscuit)",
          "Sausage Biscuit with Egg Whites (Large Biscuit)",
          "Southern Style Chicken Biscuit (Regular Biscuit)",
          "Southern Style Chicken Biscuit (Large Biscuit)",
          "Steak & Egg Biscuit (Regular Biscuit)",
          "Bacon, Egg & Cheese McGriddles",
          "Bacon, Egg & Cheese McGriddles with Egg Whites",
          "Sausage McGriddles",
          "Sausage, Egg & Cheese McGriddles",
          "Sausage, Egg & Cheese McGriddles with Egg Whites",
          "Bacon, Egg & Cheese Bagel",
          "Bacon, Egg & Cheese Bagel with Egg Whites",
          "Steak, Egg & Cheese Bagel",
          "Big Breakfast (Regular Biscuit)",
          "Big Breakfast (Large Biscuit)",
          "Big Breakfast with Egg Whites (Regular Biscuit)",
          "Big Breakfast with Egg Whites (Large Biscuit)",
          "Big Breakfast with Hotcakes (Regular Biscuit)",
          "Big Breakfast with Hotcakes (Large Biscuit)",
          "Big Breakfast with Hotcakes and Egg Whites (Regular Biscuit)",
          "Big Breakfast with Hotcakes and Egg Whites (Large Biscuit)",
          "Hotcakes",
          "Hotcakes and Sausage",
          "Sausage Burrito",
          "Hash Brown",
          "Cinnamon Melts",
          "Fruit & Maple Oatmeal",
          "Fruit & Maple Oatmeal without Brown Sugar",
          "Big Mac",
          "Quarter Pounder with Cheese",
          "Quarter Pounder with Bacon & Cheese",
          "Quarter Pounder with Bacon Habanero Ranch",
          "Quarter Pounder Deluxe",
          "Double Quarter Pounder with Cheese",
          "Hamburger",
          "Cheeseburger",
          "Double Cheeseburger",
          "Bacon Clubhouse Burger",
          "McDouble",
          "Bacon McDouble",
          "Daily Double",
          "Jalapeño Double",
          "McRib",
          "Premium Crispy Chicken Classic Sandwich",
          "Premium Grilled Chicken Classic Sandwich",
          "Premium Crispy Chicken Club Sandwich",
          "Premium Grilled Chicken Club Sandwich",
          "Premium Crispy Chicken Ranch BLT Sandwich",
          "Premium Grilled Chicken Ranch BLT Sandwich",
          "Bacon Clubhouse Crispy Chicken Sandwich",
          "Bacon Clubhouse Grilled Chicken Sandwich",
          "Southern Style Crispy Chicken Sandwich",
          "McChicken",
          "Bacon Cheddar McChicken",
          "Bacon Buffalo Ranch McChicken",
          "Buffalo Ranch McChicken",
          "Premium McWrap Chicken & Bacon (Crispy Chicken)",
          "Premium McWrap Chicken & Bacon (Grilled Chicken)",
          "Premium McWrap Chicken & Ranch (Crispy Chicken)",
          "Premium McWrap Chicken & Ranch (Grilled Chicken)",
          "Premium McWrap Southwest Chicken (Crispy Chicken)",
          "Premium McWrap Southwest Chicken (Grilled Chicken)",
          "Premium McWrap Chicken Sweet Chili (Crispy Chicken)",
          "Premium McWrap Chicken Sweet Chili (Grilled Chicken)",
          "Chicken McNuggets (4 piece)",
          "Chicken McNuggets (6 piece)",
          "Chicken McNuggets (10 piece)",
          "Chicken McNuggets (20 piece)",
          "Chicken McNuggets (40 piece)",
          "Filet-O-Fish",
          "Premium Bacon Ranch Salad (without Chicken)",
          "Premium Bacon Ranch Salad with Crispy Chicken",
          "Premium Bacon Ranch Salad with Grilled Chicken",
          "Premium Southwest Salad (without Chicken)",
          "Premium Southwest Salad with Crispy Chicken",
          "Premium Southwest Salad with Grilled Chicken",
          "Chipotle BBQ Snack Wrap (Crispy Chicken)",
          "Chipotle BBQ Snack Wrap (Grilled Chicken)",
          "Honey Mustard Snack Wrap (Crispy Chicken)",
          "Honey Mustard Snack Wrap (Grilled Chicken)",
          "Ranch Snack Wrap (Crispy Chicken)",
          "Ranch Snack Wrap (Grilled Chicken)",
          "Small French Fries",
          "Medium French Fries",
          "Large French Fries",
          "Kids French Fries",
          "Side Salad",
          "Apple Slices",
          "Fruit 'n Yogurt Parfait",
          "Baked Apple Pie",
          "Chocolate Chip Cookie",
          "Oatmeal Raisin Cookie",
          "Kids Ice Cream Cone",
          "Hot Fudge Sundae",
          "Hot Caramel Sundae",
          "Strawberry Sundae",
          "Coca-Cola Classic (Small)",
          "Coca-Cola Classic (Medium)",
          "Coca-Cola Classic (Large)",
          "Coca-Cola Classic (Child)",
          "Diet Coke (Small)",
          "Diet Coke (Medium)",
          "Diet Coke (Large)",
          "Diet Coke (Child)",
          "Dr Pepper (Small)",
          "Dr Pepper (Medium)",
          "Dr Pepper (Large)",
          "Dr Pepper (Child)",
          "Diet Dr Pepper (Small)",
          "Diet Dr Pepper (Medium)",
          "Diet Dr Pepper (Large)",
          "Diet Dr Pepper (Child)",
          "Sprite (Small)",
          "Sprite (Medium)",
          "Sprite (Large)",
          "Sprite (Child)",
          "1% Low Fat Milk Jug",
          "Fat Free Chocolate Milk Jug",
          "Minute Maid 100% Apple Juice Box",
          "Minute Maid Orange Juice (Small)",
          "Minute Maid Orange Juice (Medium)",
          "Minute Maid Orange Juice (Large)",
          "Dasani Water Bottle",
          "Iced Tea (Small)",
          "Iced Tea (Medium)",
          "Iced Tea (Large)",
          "Iced Tea (Child)",
          "Sweet Tea (Small)",
          "Sweet Tea (Medium)",
          "Sweet Tea (Large)",
          "Sweet Tea (Child)",
          "Coffee (Small)",
          "Coffee (Medium)",
          "Coffee (Large)",
          "Latte (Small)",
          "Latte (Medium)",
          "Latte (Large)",
          "Caramel Latte (Small)",
          "Caramel Latte (Medium)",
          "Caramel Latte (Large)",
          "Hazelnut Latte (Small)",
          "Hazelnut Latte (Medium)",
          "Hazelnut Latte (Large)",
          "French Vanilla Latte (Small)",
          "French Vanilla Latte (Medium)",
          "French Vanilla Latte (Large)",
          "Latte with Sugar Free French Vanilla Syrup (Small)",
          "Latte with Sugar Free French Vanilla Syrup (Medium)",
          "Latte with Sugar Free French Vanilla Syrup (Large)",
          "Nonfat Latte (Small)",
          "Nonfat Latte (Medium)",
          "Nonfat Latte (Large)",
          "Nonfat Caramel Latte (Small)",
          "Nonfat Caramel Latte (Medium)",
          "Nonfat Caramel Latte (Large)",
          "Nonfat Hazelnut Latte (Small)",
          "Nonfat Hazelnut Latte (Medium)",
          "Nonfat Hazelnut Latte (Large)",
          "Nonfat French Vanilla Latte (Small)",
          "Nonfat French Vanilla Latte (Medium)",
          "Nonfat French Vanilla Latte (Large)",
          "Nonfat Latte with Sugar Free French Vanilla Syrup (Small)",
          "Nonfat Latte with Sugar Free French Vanilla Syrup (Medium)",
          "Nonfat Latte with Sugar Free French Vanilla Syrup (Large)",
          "Mocha (Small)",
          "Mocha (Medium)",
          "Mocha (Large)",
          "Mocha with Nonfat Milk (Small)",
          "Mocha with Nonfat Milk (Medium)",
          "Mocha with Nonfat Milk (Large)",
          "Caramel Mocha (Small)",
          "Caramel Mocha (Medium)",
          "Caramel Mocha (Large)",
          "Nonfat Caramel Mocha (Small)",
          "Nonfat Caramel Mocha (Medium)",
          "Nonfat Caramel Mocha (Large)",
          "Hot Chocolate (Small)",
          "Hot Chocolate (Medium)",
          "Hot Chocolate (Large)",
          "Hot Chocolate with Nonfat Milk (Small)",
          "Hot Chocolate with Nonfat Milk (Medium)",
          "Hot Chocolate with Nonfat Milk (Large)",
          "Regular Iced Coffee (Small)",
          "Regular Iced Coffee (Medium)",
          "Regular Iced Coffee (Large)",
          "Caramel Iced Coffee (Small)",
          "Caramel Iced Coffee (Medium)",
          "Caramel Iced Coffee (Large)",
          "Hazelnut Iced Coffee (Small)",
          "Hazelnut Iced Coffee (Medium)",
          "Hazelnut Iced Coffee (Large)",
          "French Vanilla Iced Coffee (Small)",
          "French Vanilla Iced Coffee (Medium)",
          "French Vanilla Iced Coffee (Large)",
          "Iced Coffee with Sugar Free French Vanilla Syrup (Small)",
          "Iced Coffee with Sugar Free French Vanilla Syrup (Medium)",
          "Iced Coffee with Sugar Free French Vanilla Syrup (Large)",
          "Iced Mocha (Small)",
          "Iced Mocha (Medium)",
          "Iced Mocha (Large)",
          "Iced Mocha with Nonfat Milk (Small)",
          "Iced Mocha with Nonfat Milk (Medium)",
          "Iced Mocha with Nonfat Milk (Large)",
          "Iced Caramel Mocha (Small)",
          "Iced Caramel Mocha (Medium)",
          "Iced Caramel Mocha (Large)",
          "Iced Nonfat Caramel Mocha (Small)",
          "Iced Nonfat Caramel Mocha (Medium)",
          "Iced Nonfat Caramel Mocha (Large)",
          "Frappé Mocha (Small)",
          "Frappé Mocha (Medium)",
          "Frappé Mocha (Large)",
          "Frappé Caramel (Small)",
          "Frappé Caramel (Medium)",
          "Frappé Caramel (Large)",
          "Frappé Chocolate Chip (Small)",
          "Frappé Chocolate Chip (Medium)",
          "Frappé Chocolate Chip (Large)",
          "Blueberry Pomegranate Smoothie (Small)",
          "Blueberry Pomegranate Smoothie (Medium)",
          "Blueberry Pomegranate Smoothie (Large)",
          "Strawberry Banana Smoothie (Small)",
          "Strawberry Banana Smoothie (Medium)",
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     "metadata": {},
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    }
   ],
   "source": [
    "# 3D scatter plot for Total Fats\n",
    "trace1 = go.Scatter3d(\n",
    "    x=menu['Category'].values,\n",
    "    y=menu['Item'].values,\n",
    "    z=menu['Total Fat (% Daily Value)'].values,\n",
    "    text=menu['Item'].values,\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        sizemode='diameter',\n",
    "        color = menu['Total Fat (% Daily Value)'].values,\n",
    "        colorscale = 'Portland',\n",
    "        colorbar = dict(title = 'Total Fat (% Daily Value)'),\n",
    "        line=dict(color='rgb(255, 255, 255)')\n",
    "    )\n",
    ")\n",
    "\n",
    "data=[trace1]\n",
    "layout=dict(height=800, width=800, title='总脂肪（占每日推荐摄入量的百分比）')\n",
    "fig=dict(data=data, layout=layout)\n",
    "py.iplot(fig, filename='3DBubble')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "639f1170",
   "metadata": {},
   "outputs": [
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          "Big Breakfast with Hotcakes (Large Biscuit)",
          "Big Breakfast with Hotcakes and Egg Whites (Regular Biscuit)",
          "Big Breakfast with Hotcakes and Egg Whites (Large Biscuit)",
          "Hotcakes",
          "Hotcakes and Sausage",
          "Sausage Burrito",
          "Hash Brown",
          "Cinnamon Melts",
          "Fruit & Maple Oatmeal",
          "Fruit & Maple Oatmeal without Brown Sugar",
          "Big Mac",
          "Quarter Pounder with Cheese",
          "Quarter Pounder with Bacon & Cheese",
          "Quarter Pounder with Bacon Habanero Ranch",
          "Quarter Pounder Deluxe",
          "Double Quarter Pounder with Cheese",
          "Hamburger",
          "Cheeseburger",
          "Double Cheeseburger",
          "Bacon Clubhouse Burger",
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          "Daily Double",
          "Jalapeño Double",
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          "Premium Grilled Chicken Classic Sandwich",
          "Premium Crispy Chicken Club Sandwich",
          "Premium Grilled Chicken Club Sandwich",
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          "Premium Grilled Chicken Ranch BLT Sandwich",
          "Bacon Clubhouse Crispy Chicken Sandwich",
          "Bacon Clubhouse Grilled Chicken Sandwich",
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          "Bacon Buffalo Ranch McChicken",
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          "Premium McWrap Chicken & Ranch (Crispy Chicken)",
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          "Premium McWrap Southwest Chicken (Crispy Chicken)",
          "Premium McWrap Southwest Chicken (Grilled Chicken)",
          "Premium McWrap Chicken Sweet Chili (Crispy Chicken)",
          "Premium McWrap Chicken Sweet Chili (Grilled Chicken)",
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          "Chicken McNuggets (6 piece)",
          "Chicken McNuggets (10 piece)",
          "Chicken McNuggets (20 piece)",
          "Chicken McNuggets (40 piece)",
          "Filet-O-Fish",
          "Premium Bacon Ranch Salad (without Chicken)",
          "Premium Bacon Ranch Salad with Crispy Chicken",
          "Premium Bacon Ranch Salad with Grilled Chicken",
          "Premium Southwest Salad (without Chicken)",
          "Premium Southwest Salad with Crispy Chicken",
          "Premium Southwest Salad with Grilled Chicken",
          "Chipotle BBQ Snack Wrap (Crispy Chicken)",
          "Chipotle BBQ Snack Wrap (Grilled Chicken)",
          "Honey Mustard Snack Wrap (Crispy Chicken)",
          "Honey Mustard Snack Wrap (Grilled Chicken)",
          "Ranch Snack Wrap (Crispy Chicken)",
          "Ranch Snack Wrap (Grilled Chicken)",
          "Small French Fries",
          "Medium French Fries",
          "Large French Fries",
          "Kids French Fries",
          "Side Salad",
          "Apple Slices",
          "Fruit 'n Yogurt Parfait",
          "Baked Apple Pie",
          "Chocolate Chip Cookie",
          "Oatmeal Raisin Cookie",
          "Kids Ice Cream Cone",
          "Hot Fudge Sundae",
          "Hot Caramel Sundae",
          "Strawberry Sundae",
          "Coca-Cola Classic (Small)",
          "Coca-Cola Classic (Medium)",
          "Coca-Cola Classic (Large)",
          "Coca-Cola Classic (Child)",
          "Diet Coke (Small)",
          "Diet Coke (Medium)",
          "Diet Coke (Large)",
          "Diet Coke (Child)",
          "Dr Pepper (Small)",
          "Dr Pepper (Medium)",
          "Dr Pepper (Large)",
          "Dr Pepper (Child)",
          "Diet Dr Pepper (Small)",
          "Diet Dr Pepper (Medium)",
          "Diet Dr Pepper (Large)",
          "Diet Dr Pepper (Child)",
          "Sprite (Small)",
          "Sprite (Medium)",
          "Sprite (Large)",
          "Sprite (Child)",
          "1% Low Fat Milk Jug",
          "Fat Free Chocolate Milk Jug",
          "Minute Maid 100% Apple Juice Box",
          "Minute Maid Orange Juice (Small)",
          "Minute Maid Orange Juice (Medium)",
          "Minute Maid Orange Juice (Large)",
          "Dasani Water Bottle",
          "Iced Tea (Small)",
          "Iced Tea (Medium)",
          "Iced Tea (Large)",
          "Iced Tea (Child)",
          "Sweet Tea (Small)",
          "Sweet Tea (Medium)",
          "Sweet Tea (Large)",
          "Sweet Tea (Child)",
          "Coffee (Small)",
          "Coffee (Medium)",
          "Coffee (Large)",
          "Latte (Small)",
          "Latte (Medium)",
          "Latte (Large)",
          "Caramel Latte (Small)",
          "Caramel Latte (Medium)",
          "Caramel Latte (Large)",
          "Hazelnut Latte (Small)",
          "Hazelnut Latte (Medium)",
          "Hazelnut Latte (Large)",
          "French Vanilla Latte (Small)",
          "French Vanilla Latte (Medium)",
          "French Vanilla Latte (Large)",
          "Latte with Sugar Free French Vanilla Syrup (Small)",
          "Latte with Sugar Free French Vanilla Syrup (Medium)",
          "Latte with Sugar Free French Vanilla Syrup (Large)",
          "Nonfat Latte (Small)",
          "Nonfat Latte (Medium)",
          "Nonfat Latte (Large)",
          "Nonfat Caramel Latte (Small)",
          "Nonfat Caramel Latte (Medium)",
          "Nonfat Caramel Latte (Large)",
          "Nonfat Hazelnut Latte (Small)",
          "Nonfat Hazelnut Latte (Medium)",
          "Nonfat Hazelnut Latte (Large)",
          "Nonfat French Vanilla Latte (Small)",
          "Nonfat French Vanilla Latte (Medium)",
          "Nonfat French Vanilla Latte (Large)",
          "Nonfat Latte with Sugar Free French Vanilla Syrup (Small)",
          "Nonfat Latte with Sugar Free French Vanilla Syrup (Medium)",
          "Nonfat Latte with Sugar Free French Vanilla Syrup (Large)",
          "Mocha (Small)",
          "Mocha (Medium)",
          "Mocha (Large)",
          "Mocha with Nonfat Milk (Small)",
          "Mocha with Nonfat Milk (Medium)",
          "Mocha with Nonfat Milk (Large)",
          "Caramel Mocha (Small)",
          "Caramel Mocha (Medium)",
          "Caramel Mocha (Large)",
          "Nonfat Caramel Mocha (Small)",
          "Nonfat Caramel Mocha (Medium)",
          "Nonfat Caramel Mocha (Large)",
          "Hot Chocolate (Small)",
          "Hot Chocolate (Medium)",
          "Hot Chocolate (Large)",
          "Hot Chocolate with Nonfat Milk (Small)",
          "Hot Chocolate with Nonfat Milk (Medium)",
          "Hot Chocolate with Nonfat Milk (Large)",
          "Regular Iced Coffee (Small)",
          "Regular Iced Coffee (Medium)",
          "Regular Iced Coffee (Large)",
          "Caramel Iced Coffee (Small)",
          "Caramel Iced Coffee (Medium)",
          "Caramel Iced Coffee (Large)",
          "Hazelnut Iced Coffee (Small)",
          "Hazelnut Iced Coffee (Medium)",
          "Hazelnut Iced Coffee (Large)",
          "French Vanilla Iced Coffee (Small)",
          "French Vanilla Iced Coffee (Medium)",
          "French Vanilla Iced Coffee (Large)",
          "Iced Coffee with Sugar Free French Vanilla Syrup (Small)",
          "Iced Coffee with Sugar Free French Vanilla Syrup (Medium)",
          "Iced Coffee with Sugar Free French Vanilla Syrup (Large)",
          "Iced Mocha (Small)",
          "Iced Mocha (Medium)",
          "Iced Mocha (Large)",
          "Iced Mocha with Nonfat Milk (Small)",
          "Iced Mocha with Nonfat Milk (Medium)",
          "Iced Mocha with Nonfat Milk (Large)",
          "Iced Caramel Mocha (Small)",
          "Iced Caramel Mocha (Medium)",
          "Iced Caramel Mocha (Large)",
          "Iced Nonfat Caramel Mocha (Small)",
          "Iced Nonfat Caramel Mocha (Medium)",
          "Iced Nonfat Caramel Mocha (Large)",
          "Frappé Mocha (Small)",
          "Frappé Mocha (Medium)",
          "Frappé Mocha (Large)",
          "Frappé Caramel (Small)",
          "Frappé Caramel (Medium)",
          "Frappé Caramel (Large)",
          "Frappé Chocolate Chip (Small)",
          "Frappé Chocolate Chip (Medium)",
          "Frappé Chocolate Chip (Large)",
          "Blueberry Pomegranate Smoothie (Small)",
          "Blueberry Pomegranate Smoothie (Medium)",
          "Blueberry Pomegranate Smoothie (Large)",
          "Strawberry Banana Smoothie (Small)",
          "Strawberry Banana Smoothie (Medium)",
          "Strawberry Banana Smoothie (Large)",
          "Mango Pineapple Smoothie (Small)",
          "Mango Pineapple Smoothie (Medium)",
          "Mango Pineapple Smoothie (Large)",
          "Vanilla Shake (Small)",
          "Vanilla Shake (Medium)",
          "Vanilla Shake (Large)",
          "Strawberry Shake (Small)",
          "Strawberry Shake (Medium)",
          "Strawberry Shake (Large)",
          "Chocolate Shake (Small)",
          "Chocolate Shake (Medium)",
          "Chocolate Shake (Large)",
          "Shamrock Shake (Medium)",
          "Shamrock Shake (Large)",
          "McFlurry with M&M’s Candies (Small)",
          "McFlurry with M&M’s Candies (Medium)",
          "McFlurry with M&M’s Candies (Snack)",
          "McFlurry with Oreo Cookies (Small)",
          "McFlurry with Oreo Cookies (Medium)",
          "McFlurry with Oreo Cookies (Snack)",
          "McFlurry with Reese's Peanut Butter Cups (Medium)",
          "McFlurry with Reese's Peanut Butter Cups (Snack)"
         ],
         "type": "scatter3d",
         "x": [
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Breakfast",
          "Beef & Pork",
          "Beef & Pork",
          "Beef & Pork",
          "Beef & Pork",
          "Beef & Pork",
          "Beef & Pork",
          "Beef & Pork",
          "Beef & Pork",
          "Beef & Pork",
          "Beef & Pork",
          "Beef & Pork",
          "Beef & Pork",
          "Beef & Pork",
          "Beef & Pork",
          "Beef & Pork",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Chicken & Fish",
          "Salads",
          "Salads",
          "Salads",
          "Salads",
          "Salads",
          "Salads",
          "Snacks & Sides",
          "Snacks & Sides",
          "Snacks & Sides",
          "Snacks & Sides",
          "Snacks & Sides",
          "Snacks & Sides",
          "Snacks & Sides",
          "Snacks & Sides",
          "Snacks & Sides",
          "Snacks & Sides",
          "Snacks & Sides",
          "Snacks & Sides",
          "Snacks & Sides",
          "Desserts",
          "Desserts",
          "Desserts",
          "Desserts",
          "Desserts",
          "Desserts",
          "Desserts",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Beverages",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Coffee & Tea",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes",
          "Smoothies & Shakes"
         ],
         "y": [
          "Egg McMuffin",
          "Egg White Delight",
          "Sausage McMuffin",
          "Sausage McMuffin with Egg",
          "Sausage McMuffin with Egg Whites",
          "Steak & Egg McMuffin",
          "Bacon, Egg & Cheese Biscuit (Regular Biscuit)",
          "Bacon, Egg & Cheese Biscuit (Large Biscuit)",
          "Bacon, Egg & Cheese Biscuit with Egg Whites (Regular Biscuit)",
          "Bacon, Egg & Cheese Biscuit with Egg Whites (Large Biscuit)",
          "Sausage Biscuit (Regular Biscuit)",
          "Sausage Biscuit (Large Biscuit)",
          "Sausage Biscuit with Egg (Regular Biscuit)",
          "Sausage Biscuit with Egg (Large Biscuit)",
          "Sausage Biscuit with Egg Whites (Regular Biscuit)",
          "Sausage Biscuit with Egg Whites (Large Biscuit)",
          "Southern Style Chicken Biscuit (Regular Biscuit)",
          "Southern Style Chicken Biscuit (Large Biscuit)",
          "Steak & Egg Biscuit (Regular Biscuit)",
          "Bacon, Egg & Cheese McGriddles",
          "Bacon, Egg & Cheese McGriddles with Egg Whites",
          "Sausage McGriddles",
          "Sausage, Egg & Cheese McGriddles",
          "Sausage, Egg & Cheese McGriddles with Egg Whites",
          "Bacon, Egg & Cheese Bagel",
          "Bacon, Egg & Cheese Bagel with Egg Whites",
          "Steak, Egg & Cheese Bagel",
          "Big Breakfast (Regular Biscuit)",
          "Big Breakfast (Large Biscuit)",
          "Big Breakfast with Egg Whites (Regular Biscuit)",
          "Big Breakfast with Egg Whites (Large Biscuit)",
          "Big Breakfast with Hotcakes (Regular Biscuit)",
          "Big Breakfast with Hotcakes (Large Biscuit)",
          "Big Breakfast with Hotcakes and Egg Whites (Regular Biscuit)",
          "Big Breakfast with Hotcakes and Egg Whites (Large Biscuit)",
          "Hotcakes",
          "Hotcakes and Sausage",
          "Sausage Burrito",
          "Hash Brown",
          "Cinnamon Melts",
          "Fruit & Maple Oatmeal",
          "Fruit & Maple Oatmeal without Brown Sugar",
          "Big Mac",
          "Quarter Pounder with Cheese",
          "Quarter Pounder with Bacon & Cheese",
          "Quarter Pounder with Bacon Habanero Ranch",
          "Quarter Pounder Deluxe",
          "Double Quarter Pounder with Cheese",
          "Hamburger",
          "Cheeseburger",
          "Double Cheeseburger",
          "Bacon Clubhouse Burger",
          "McDouble",
          "Bacon McDouble",
          "Daily Double",
          "Jalapeño Double",
          "McRib",
          "Premium Crispy Chicken Classic Sandwich",
          "Premium Grilled Chicken Classic Sandwich",
          "Premium Crispy Chicken Club Sandwich",
          "Premium Grilled Chicken Club Sandwich",
          "Premium Crispy Chicken Ranch BLT Sandwich",
          "Premium Grilled Chicken Ranch BLT Sandwich",
          "Bacon Clubhouse Crispy Chicken Sandwich",
          "Bacon Clubhouse Grilled Chicken Sandwich",
          "Southern Style Crispy Chicken Sandwich",
          "McChicken",
          "Bacon Cheddar McChicken",
          "Bacon Buffalo Ranch McChicken",
          "Buffalo Ranch McChicken",
          "Premium McWrap Chicken & Bacon (Crispy Chicken)",
          "Premium McWrap Chicken & Bacon (Grilled Chicken)",
          "Premium McWrap Chicken & Ranch (Crispy Chicken)",
          "Premium McWrap Chicken & Ranch (Grilled Chicken)",
          "Premium McWrap Southwest Chicken (Crispy Chicken)",
          "Premium McWrap Southwest Chicken (Grilled Chicken)",
          "Premium McWrap Chicken Sweet Chili (Crispy Chicken)",
          "Premium McWrap Chicken Sweet Chili (Grilled Chicken)",
          "Chicken McNuggets (4 piece)",
          "Chicken McNuggets (6 piece)",
          "Chicken McNuggets (10 piece)",
          "Chicken McNuggets (20 piece)",
          "Chicken McNuggets (40 piece)",
          "Filet-O-Fish",
          "Premium Bacon Ranch Salad (without Chicken)",
          "Premium Bacon Ranch Salad with Crispy Chicken",
          "Premium Bacon Ranch Salad with Grilled Chicken",
          "Premium Southwest Salad (without Chicken)",
          "Premium Southwest Salad with Crispy Chicken",
          "Premium Southwest Salad with Grilled Chicken",
          "Chipotle BBQ Snack Wrap (Crispy Chicken)",
          "Chipotle BBQ Snack Wrap (Grilled Chicken)",
          "Honey Mustard Snack Wrap (Crispy Chicken)",
          "Honey Mustard Snack Wrap (Grilled Chicken)",
          "Ranch Snack Wrap (Crispy Chicken)",
          "Ranch Snack Wrap (Grilled Chicken)",
          "Small French Fries",
          "Medium French Fries",
          "Large French Fries",
          "Kids French Fries",
          "Side Salad",
          "Apple Slices",
          "Fruit 'n Yogurt Parfait",
          "Baked Apple Pie",
          "Chocolate Chip Cookie",
          "Oatmeal Raisin Cookie",
          "Kids Ice Cream Cone",
          "Hot Fudge Sundae",
          "Hot Caramel Sundae",
          "Strawberry Sundae",
          "Coca-Cola Classic (Small)",
          "Coca-Cola Classic (Medium)",
          "Coca-Cola Classic (Large)",
          "Coca-Cola Classic (Child)",
          "Diet Coke (Small)",
          "Diet Coke (Medium)",
          "Diet Coke (Large)",
          "Diet Coke (Child)",
          "Dr Pepper (Small)",
          "Dr Pepper (Medium)",
          "Dr Pepper (Large)",
          "Dr Pepper (Child)",
          "Diet Dr Pepper (Small)",
          "Diet Dr Pepper (Medium)",
          "Diet Dr Pepper (Large)",
          "Diet Dr Pepper (Child)",
          "Sprite (Small)",
          "Sprite (Medium)",
          "Sprite (Large)",
          "Sprite (Child)",
          "1% Low Fat Milk Jug",
          "Fat Free Chocolate Milk Jug",
          "Minute Maid 100% Apple Juice Box",
          "Minute Maid Orange Juice (Small)",
          "Minute Maid Orange Juice (Medium)",
          "Minute Maid Orange Juice (Large)",
          "Dasani Water Bottle",
          "Iced Tea (Small)",
          "Iced Tea (Medium)",
          "Iced Tea (Large)",
          "Iced Tea (Child)",
          "Sweet Tea (Small)",
          "Sweet Tea (Medium)",
          "Sweet Tea (Large)",
          "Sweet Tea (Child)",
          "Coffee (Small)",
          "Coffee (Medium)",
          "Coffee (Large)",
          "Latte (Small)",
          "Latte (Medium)",
          "Latte (Large)",
          "Caramel Latte (Small)",
          "Caramel Latte (Medium)",
          "Caramel Latte (Large)",
          "Hazelnut Latte (Small)",
          "Hazelnut Latte (Medium)",
          "Hazelnut Latte (Large)",
          "French Vanilla Latte (Small)",
          "French Vanilla Latte (Medium)",
          "French Vanilla Latte (Large)",
          "Latte with Sugar Free French Vanilla Syrup (Small)",
          "Latte with Sugar Free French Vanilla Syrup (Medium)",
          "Latte with Sugar Free French Vanilla Syrup (Large)",
          "Nonfat Latte (Small)",
          "Nonfat Latte (Medium)",
          "Nonfat Latte (Large)",
          "Nonfat Caramel Latte (Small)",
          "Nonfat Caramel Latte (Medium)",
          "Nonfat Caramel Latte (Large)",
          "Nonfat Hazelnut Latte (Small)",
          "Nonfat Hazelnut Latte (Medium)",
          "Nonfat Hazelnut Latte (Large)",
          "Nonfat French Vanilla Latte (Small)",
          "Nonfat French Vanilla Latte (Medium)",
          "Nonfat French Vanilla Latte (Large)",
          "Nonfat Latte with Sugar Free French Vanilla Syrup (Small)",
          "Nonfat Latte with Sugar Free French Vanilla Syrup (Medium)",
          "Nonfat Latte with Sugar Free French Vanilla Syrup (Large)",
          "Mocha (Small)",
          "Mocha (Medium)",
          "Mocha (Large)",
          "Mocha with Nonfat Milk (Small)",
          "Mocha with Nonfat Milk (Medium)",
          "Mocha with Nonfat Milk (Large)",
          "Caramel Mocha (Small)",
          "Caramel Mocha (Medium)",
          "Caramel Mocha (Large)",
          "Nonfat Caramel Mocha (Small)",
          "Nonfat Caramel Mocha (Medium)",
          "Nonfat Caramel Mocha (Large)",
          "Hot Chocolate (Small)",
          "Hot Chocolate (Medium)",
          "Hot Chocolate (Large)",
          "Hot Chocolate with Nonfat Milk (Small)",
          "Hot Chocolate with Nonfat Milk (Medium)",
          "Hot Chocolate with Nonfat Milk (Large)",
          "Regular Iced Coffee (Small)",
          "Regular Iced Coffee (Medium)",
          "Regular Iced Coffee (Large)",
          "Caramel Iced Coffee (Small)",
          "Caramel Iced Coffee (Medium)",
          "Caramel Iced Coffee (Large)",
          "Hazelnut Iced Coffee (Small)",
          "Hazelnut Iced Coffee (Medium)",
          "Hazelnut Iced Coffee (Large)",
          "French Vanilla Iced Coffee (Small)",
          "French Vanilla Iced Coffee (Medium)",
          "French Vanilla Iced Coffee (Large)",
          "Iced Coffee with Sugar Free French Vanilla Syrup (Small)",
          "Iced Coffee with Sugar Free French Vanilla Syrup (Medium)",
          "Iced Coffee with Sugar Free French Vanilla Syrup (Large)",
          "Iced Mocha (Small)",
          "Iced Mocha (Medium)",
          "Iced Mocha (Large)",
          "Iced Mocha with Nonfat Milk (Small)",
          "Iced Mocha with Nonfat Milk (Medium)",
          "Iced Mocha with Nonfat Milk (Large)",
          "Iced Caramel Mocha (Small)",
          "Iced Caramel Mocha (Medium)",
          "Iced Caramel Mocha (Large)",
          "Iced Nonfat Caramel Mocha (Small)",
          "Iced Nonfat Caramel Mocha (Medium)",
          "Iced Nonfat Caramel Mocha (Large)",
          "Frappé Mocha (Small)",
          "Frappé Mocha (Medium)",
          "Frappé Mocha (Large)",
          "Frappé Caramel (Small)",
          "Frappé Caramel (Medium)",
          "Frappé Caramel (Large)",
          "Frappé Chocolate Chip (Small)",
          "Frappé Chocolate Chip (Medium)",
          "Frappé Chocolate Chip (Large)",
          "Blueberry Pomegranate Smoothie (Small)",
          "Blueberry Pomegranate Smoothie (Medium)",
          "Blueberry Pomegranate Smoothie (Large)",
          "Strawberry Banana Smoothie (Small)",
          "Strawberry Banana Smoothie (Medium)",
          "Strawberry Banana Smoothie (Large)",
          "Mango Pineapple Smoothie (Small)",
          "Mango Pineapple Smoothie (Medium)",
          "Mango Pineapple Smoothie (Large)",
          "Vanilla Shake (Small)",
          "Vanilla Shake (Medium)",
          "Vanilla Shake (Large)",
          "Strawberry Shake (Small)",
          "Strawberry Shake (Medium)",
          "Strawberry Shake (Large)",
          "Chocolate Shake (Small)",
          "Chocolate Shake (Medium)",
          "Chocolate Shake (Large)",
          "Shamrock Shake (Medium)",
          "Shamrock Shake (Large)",
          "McFlurry with M&M’s Candies (Small)",
          "McFlurry with M&M’s Candies (Medium)",
          "McFlurry with M&M’s Candies (Snack)",
          "McFlurry with Oreo Cookies (Small)",
          "McFlurry with Oreo Cookies (Medium)",
          "McFlurry with Oreo Cookies (Snack)",
          "McFlurry with Reese's Peanut Butter Cups (Medium)",
          "McFlurry with Reese's Peanut Butter Cups (Snack)"
         ],
         "z": [
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3D scatter plot for Carbohydrate\n",
    "trace1 = go.Scatter3d(\n",
    "    x=menu['Category'].values,\n",
    "    y=menu['Item'].values,\n",
    "    z=menu['Carbohydrates (% Daily Value)'].values,\n",
    "    text=menu['Item'].values,\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        sizemode='diameter',\n",
    "        color = menu['Carbohydrates (% Daily Value)'].values,\n",
    "        colorscale = 'Portland',\n",
    "        colorbar = dict(title = 'Carbohydrates (% Daily Value)'),\n",
    "        line=dict(color='rgb(255, 255, 255)')\n",
    "    )\n",
    ")\n",
    "\n",
    "data=[trace1]\n",
    "layout=dict(height=800, width=800, title='碳水化合物（占每日推荐摄入量的百分比）')\n",
    "fig=dict(data=data, layout=layout)\n",
    "py.iplot(fig, filename='3DBubble')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a1a9671",
   "metadata": {},
   "source": [
    "### 每个麦当劳产品的膳食纤维，钙和铁（占每日推荐摄入量的百分比）散点图\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9cda4e50",
   "metadata": {},
   "outputs": [
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          "Sausage Biscuit (Large Biscuit)",
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          "Sausage Biscuit with Egg (Large Biscuit)",
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          "Sausage Biscuit with Egg Whites (Large Biscuit)",
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Fiber (% Daily Value)\"},\"zeroline\":false}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('371f6f54-cfd6-4408-8aaf-5ccb2fefe6df');\n",
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       "            observer.disconnect();\n",
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       "var notebookContainer = gd.closest('#notebook-container');\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Dietary Fiber Scatter plots\n",
    "trace = go.Scatter(\n",
    "    y = menu['Dietary Fiber (% Daily Value)'].values,\n",
    "    x = menu['Item'].values,\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        size= menu['Dietary Fiber (% Daily Value)'].values,\n",
    "        color = menu['Dietary Fiber (% Daily Value)'].values,\n",
    "        colorscale='Portland',\n",
    "        reversescale = True,\n",
    "        showscale=True\n",
    "    ),\n",
    "    text = menu['Item'].values\n",
    ")\n",
    "data = [trace]\n",
    "\n",
    "layout= go.Layout(\n",
    "    autosize= True,\n",
    "    title= '膳食纤维（占每日推荐摄入量的百分比）',\n",
    "    hovermode= 'closest',\n",
    "        xaxis=dict(\n",
    "        showgrid=False,\n",
    "        zeroline=False,\n",
    "        showline=False\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        title= 'Dietary Fiber (% Daily Value)',\n",
    "        ticklen= 5,\n",
    "        gridwidth= 2,\n",
    "        showgrid=False,\n",
    "        zeroline=False,\n",
    "        showline=False,\n",
    "    ),\n",
    "    showlegend= False\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig,filename='scatterChol')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b62e68b3",
   "metadata": {},
   "outputs": [
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         "text": [
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          "Sausage McMuffin with Egg",
          "Sausage McMuffin with Egg Whites",
          "Steak & Egg McMuffin",
          "Bacon, Egg & Cheese Biscuit (Regular Biscuit)",
          "Bacon, Egg & Cheese Biscuit (Large Biscuit)",
          "Bacon, Egg & Cheese Biscuit with Egg Whites (Regular Biscuit)",
          "Bacon, Egg & Cheese Biscuit with Egg Whites (Large Biscuit)",
          "Sausage Biscuit (Regular Biscuit)",
          "Sausage Biscuit (Large Biscuit)",
          "Sausage Biscuit with Egg (Regular Biscuit)",
          "Sausage Biscuit with Egg (Large Biscuit)",
          "Sausage Biscuit with Egg Whites (Regular Biscuit)",
          "Sausage Biscuit with Egg Whites (Large Biscuit)",
          "Southern Style Chicken Biscuit (Regular Biscuit)",
          "Southern Style Chicken Biscuit (Large Biscuit)",
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(% Daily Value)\"},\"zeroline\":false}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calcium Scatter plots\n",
    "trace = go.Scatter(\n",
    "    y = menu['Calcium (% Daily Value)'].values,\n",
    "    x = menu['Item'].values,\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        size= menu['Calcium (% Daily Value)'].values,\n",
    "        color = menu['Calcium (% Daily Value)'].values,\n",
    "        colorscale='Portland',\n",
    "        reversescale = True,\n",
    "        showscale=True\n",
    "    ),\n",
    "    text = menu['Item'].values\n",
    ")\n",
    "data = [trace]\n",
    "\n",
    "layout= go.Layout(\n",
    "    autosize= True,\n",
    "    title= '钙（占每日推荐摄入量的百分比）',\n",
    "    hovermode= 'closest',\n",
    "        xaxis=dict(\n",
    "        showgrid=False,\n",
    "        zeroline=False,\n",
    "        showline=False\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        title= 'Calcium (% Daily Value)',\n",
    "        ticklen= 5,\n",
    "        gridwidth= 2,\n",
    "        showgrid=False,\n",
    "        zeroline=False,\n",
    "        showline=False,\n",
    "    ),\n",
    "    showlegend= False\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig,filename='scatterChol')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cb8b0dfa",
   "metadata": {},
   "outputs": [
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         "text": [
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          "Sausage McMuffin",
          "Sausage McMuffin with Egg",
          "Sausage McMuffin with Egg Whites",
          "Steak & Egg McMuffin",
          "Bacon, Egg & Cheese Biscuit (Regular Biscuit)",
          "Bacon, Egg & Cheese Biscuit (Large Biscuit)",
          "Bacon, Egg & Cheese Biscuit with Egg Whites (Regular Biscuit)",
          "Bacon, Egg & Cheese Biscuit with Egg Whites (Large Biscuit)",
          "Sausage Biscuit (Regular Biscuit)",
          "Sausage Biscuit (Large Biscuit)",
          "Sausage Biscuit with Egg (Regular Biscuit)",
          "Sausage Biscuit with Egg (Large Biscuit)",
          "Sausage Biscuit with Egg Whites (Regular Biscuit)",
          "Sausage Biscuit with Egg Whites (Large Biscuit)",
          "Southern Style Chicken Biscuit (Regular Biscuit)",
          "Southern Style Chicken Biscuit (Large Biscuit)",
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          "Nonfat Hazelnut Latte (Large)",
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      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "# Iron Scatter plots\n",
    "trace = go.Scatter(\n",
    "    y = menu['Iron (% Daily Value)'].values,\n",
    "    x = menu['Item'].values,\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        size= menu['Iron (% Daily Value)'].values,\n",
    "        #color = np.random.randn(500), #set color equal to a variable\n",
    "        color = menu['Iron (% Daily Value)'].values,\n",
    "        colorscale='Portland',\n",
    "        reversescale = True,\n",
    "        showscale=True\n",
    "    ),\n",
    "    text = menu['Item'].values\n",
    ")\n",
    "data = [trace]\n",
    "\n",
    "layout= go.Layout(\n",
    "    autosize= True,\n",
    "    title= '铁（占每日推荐摄入量的百分比）',\n",
    "    hovermode= 'closest',\n",
    "        xaxis=dict(\n",
    "        showgrid=False,\n",
    "        zeroline=False,\n",
    "        showline=False\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        title= 'Iron (% Daily Value)',\n",
    "        ticklen= 5,\n",
    "        gridwidth= 2,\n",
    "        showgrid=False,\n",
    "        zeroline=False,\n",
    "        showline=False,\n",
    "    ),\n",
    "    showlegend= False\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig,filename='scatterChol')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "311c10ab",
   "metadata": {},
   "source": [
    "一些有趣的发现：\n",
    "相比于不健康营养元素的“频频超标”，健康营养元素的含量不尽如人意。没有任何食物的膳食纤维（占每日推荐摄入量的百分比）超过30%，铁超过40%。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b17ff5cf",
   "metadata": {},
   "source": [
    "### 每种食品的卡路里含量：互动条形图\n",
    "最后，我们绘制条形图，从高到低展示每种食品的卡路里含量。先按照卡路里含量从高到低对食品排序，再使用Plotly绘制互动条形图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7c3ba615",
   "metadata": {},
   "outputs": [
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       "                            \n",
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       "var x = new MutationObserver(function (mutations, observer) {{\n",
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       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "    x.observe(outputEl, {childList: true});\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#按照卡路里含量对食品排序\n",
    "x, y = (list(x) for x in zip(*sorted(zip(menu.Calories.values, menu.Item.values), reverse = False)))\n",
    "# 绘制条形图                                                      \n",
    "trace2 = go.Bar(\n",
    "    x=x ,\n",
    "    y=y,\n",
    "    marker=dict(\n",
    "        color=x,\n",
    "        colorscale = 'Jet',\n",
    "        reversescale = False\n",
    "    ),\n",
    "    name='Household savings, percentage of household disposable income',\n",
    "    orientation='h',\n",
    ")\n",
    "\n",
    "layout = dict(\n",
    "    title='麦当劳食品的卡路里含量',\n",
    "     width = 1500, height = 2600,\n",
    "    yaxis=dict(\n",
    "        showgrid=False,\n",
    "        showline=False,\n",
    "        showticklabels=True,\n",
    "#         domain=[0, 0.85],\n",
    "    ))\n",
    "\n",
    "fig1 = go.Figure(data=[trace2])\n",
    "fig1['layout'].update(layout)\n",
    "py.iplot(fig1, filename='plots')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "267c460c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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